Challenges of Big Data Analytics in the Healthcare Industry

The demand for data conversion services and data cleansing services is steadily increasing with the increasing number of digital initiatives in organizations. The rapidly expanding field of Big Data analytics has started to play a pivotal role in the evolution of healthcare practices and research. It provides tools to accumulate, manage, analyze and assimilate large volumes of disparate, structured and unstructured data produced by the current healthcare system.

Big data in healthcare refers to electronic health data sets that are large, complex, and difficult to manage with traditional software. It refers to the ability to collect and analyze the vast amounts of data. Here are some of the challenges in analyzing big data once it is captured.

• Querying data: Storing and querying massive data can be time consuming and expensive. The ability to query data is foundational for reporting and analytics. But healthcare organizations should overcome the challenges before they engage in meaningful analysis of their big data assets. The healthcare organization must overcome data silos and interoperability issues that prevent query tools from accessing the organization’s entire data repository. It may not be possible to create a complete portrait of an organization or an individual patient’s health if the different components of a dataset are held in multiple walled off systems or in different formats. Many healthcare organizations use Structured Query Language to dive into large datasets, but the data becomes useful only if it is accurate and complete.
• Reporting data: Once the query process is over, the next process is generating a report that is clear. Again, accuracy and integrity of data are essential to ensure that the report is reliable. Poor data can produce only poor reports which could confuse the physicians who are trying to offer quality treatment to the patients. Reporting refers to perquisites for analysis i.e. extracting data before it is examined. While some reports may be geared towards highlighting a certain trend, others must be presented in a way that allows the readers to draw his or her own inferences about what the full spectrum of data means. Organizations should provide a clear picture about how they plan to use their reports to ensure that database administration can generate the information they actually need. The process of reporting in healthcare industry is external since regulatory and quality assessment programs frequently demand large volumes of data to feed quality measures and reimbursement models. Providers have many options for meeting these various requirements, including qualified registries, reporting tools built into the electronic health records and web portals hosted by CMS and other groups.
• Visualization of data: Visualization of data can make it easier for a clinician to absorb information and use it appropriately. One of the most popular data visualization techniques is colour coding and it immediately produces results. Using colours like red, yellow and green are universally understood to mean stop, caution, and go. Healthcare organizations should have good data presentation practices such as use of charts and proper proportion to illustrate contrasting figures, proper labelling of information to reduce potential confusion. Use of convoluted flowcharts, cramped or overlapping text and low quality graphics can annoy recipients, which can lead them to ignore or misinterpret data.
• Data updating: Healthcare data is not static, it constantly requires update in order to remain current and relevant. In some cases datasets keep changing like patient vital signs, which may occur every few seconds. Other information also keep changing like contact details, address, marital status but only a very few times in an individual’s entire life time. It is challenge for healthcare organizations to understand the volatility of big data or how often it changes or to what degree it changes. Healthcare providers should have a clear idea about which dataset needs manual updating, which can be automated, and how to complete the process without downtime for end users, and how to ensure that updates can be conducted with harming the quality of the data. Organizations should ensure that not to create unnecessary duplicate records when attempting and update data.
• Sharing of data: Some providers operate in a vacuum and some patients will receive all of their care at a single location. This means that sharing data with external partners is important as the industry moves towards population health management and value-based care. For organizations of all types, sizes and positions along the data maturity spectrum, data interoperability is a perennial concern. Fundamental differences in the way electronic health records are designed and implemented can severely restrict the ability to move data between different organizations. It often leaves clinicians without the information they need to make key decisions, follow up with patients and develop strategies to improve overall outcomes. The healthcare industry is trying hard to improve sharing of data across technical and organizational barriers. New advanced tools like FHIR and public APIs as well as partnerships like Common Well and CareQuality are making it easier for developers to share data easily and securely.

For developing a big data exchange ecosystem that connects all members of the care team with meaningful and timely information, providers will have to overcome every challenge. To improve efficiency and ensure systematic workflow in a healthcare organization, all paper-based data can be converted into digital format with the help of data conversion services. Unstructured data residing in these documents can be extracted, cleaned and analyzed with data cleansing services – this will ensure clean and reliable, actionable data.